mhd-medfa/NoisyStudent-Based-Object-Recognition

This helps train an object recognition model to accurately identify nine common objects like cars, birds, and ships, even when your training data comes from slightly different visual environments. It takes in images from both a labeled source and a related, unlabeled source, and outputs a refined model capable of classifying images more robustly. This is useful for data scientists or machine learning engineers working on image classification tasks with varied or domain-shifted datasets.

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